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We have all heard about the totally unhip GDPR and the potential wave of fines and lawsuits. The long arm of the law and it’s stick have been noted. Less talked about but infinitely more exciting is the other side. Turn over the coin and there’s a whole A-Z of organisational and employee carrots. How so?

We all leave digital trails behind us, trails about us. Others that have access to these trails can use our data and information. The new European General Data Protection Regulation (GDPR) intends the usage of such Personal Identifiable Information (PII) to be correct and regulated, with the power to decide given to the individual.

Some organisations are wondering how on earth they can become GDPR compliant when they already have a business to run. But instead of a chore, setting a pathway to allow for some more principled digital organisational housekeeping can bring big organisational gains sooner rather than later.

Many enterprises are now beginning to realise the extra potential gains of having introduced new organisational principles to become compliant. The initial fear of painful change soon subsides when the better quality data comes along to make business life easier. With the further experience of new initiatives from new data analysis, NLP, deep learning, AI, comes the feeling: why we didn’t we just do this sooner?

Most organisations have a system(s) in place holding PII data, even if getting the right data out in the right format remains problematical. The organisation of data for GDPR compliance can be best achieved so that it becomes transformed to be part of a semantic data layer. With such a layer, knowing all the related data from different sources you have on Joe Bloggs becomes so much easier when he asks for a copy of the data you have about him. Such a semantic data layer will also bring other far-reaching and organisation-wide benefits.

Semantic Data Layer

For example, heterogeneous data in different formats and from different sources can become unified for all sorts of new smart applications, new insights and new innovation that would have been previously unthinkable. Data can stay where it is… no need to change that relational database yet again because of a new type of data. The same information principles and technologies involved in keeping an eye on PII use, can also be used to improve processes or efficiencies and detect consumer behaviour or market changes.

But it’s not just the business operations that benefit, empowered employees become happier having the right information at hand to do their job. Something that is often difficult to achieve, as in many organisations, no one area “owns” search, making it is usually somebody else’s problem to solve. For the Google-loving employee, not finding stuff at work to help them in their job can be downright frustrating. Well ordered data (better still in a semantic layer) can give them the empowering results page they need. It’s easy to forget that Google only deals with the best structured and linked documentation, why shouldn’t we do the same in our organisations?

Just as the combination of (previously heterogeneous) datasets can give us new insights for innovation, we also observe that innovation increasingly comes in the form of external collaboration. Such collaboration of course increases the potential GDPR risk through data sharing, Facebook being a very current point in case. This brings in the need for organisational policy covering data access, the use and handling of existing data and any new (extra) data created through its use. Such policy should for example cover newly created personal data from statistical inference analysis.

While having a semantic layer may in fact make human error in data usage potentially more possible through increased access, it also provides a better potential solution to prevent misuse as metadata can be baked into the data to classify both information “sensitivity” and control user accessibility rights.

Secondly start small, apply organising principles by focusing on the low-hanging fruit: the already structured data within systems. The creation of quality data with added metadata in a semantic layer can have a magnetic effect within an organisation (build that semantic platform and they will come).

Step three: start being creative and agile.

A case story

A recent case, within the insurance industry reveals some cues to why these set of tools will improve signals and attention for becoming more compliant with regulations dealing with PII. Our client knew about a set of collections (file shares) where PII might be found. Adding search, and NLP/ML opened up the pandoras box with visual analytic tools. This is the simple starting point, finding i.e names or personal number concepts in the text. Second to this will be to add semantics, where industry standard terminologies and ontologies can further help define the meaning of things.

In all corporate settings, there exist both well-cultivated and governed collections of information resources, but usually also a massive unmapped terrain of content collections, where no one has a clue if there might be PII hidden amongst it. The strategy using a semantic data layer should always be combined with operations to narrowing down the collections to become part of the signalling system – it is generally not a good idea to boil the whole-data-ocean in the enterprise information environment. Rather through such work practices, workers are aware of the data hot-spots, the well-cultivated collections of information and that unmapped terrain. Having the additional notion of PII to contend with will make it that just bit easier to recognise those places where semantic enhancement is needed.

not a good idea to boil the whole-data-ocean

Running with the same pipeline (with the option of further models to refine and improve certain data) will not only allow for the discovery of multiple occurrences of named entities (individuals) but also the narrative and context in which they appear.
Having a targeted model & terminology for the insurance industry will only go to improve this semantic process further. This process can certainly ease what may be currently manual processes or processes that don’t exist because of their manual pain: for example, finding sensitive textual information from documents within applications or from online textual chats. Developing such a smart information platform enables the smarter linking of other things from the model, such as service packages, service units / or organisational entities, spatial data as named places or timelines, or medical treatments, things perhaps currently you have less control over.

There’s not much time before the 25th May and the new GDPR, but we’ll still be here afterwards to help you with a compliance burden or a creative pathway, depending on your outlook.

This is the seventh post in a series (1, 2, 3, 4, 5, 6) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide on the steps you need to take.

Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example. Planning for migration.

Do not even think about moving into the cloud apartment without a proper cleaning of the content buckets. Moving from an architected household to a rented place, taxes a structured audit. Clean out all redundant, outdated and trivial matter (ROT). The very same habit you have cleaning up the attic when moving out from your old house.

It is also a good idea to decorate and add any features to your new cloud apartment before the content furniture is there. It means the content will fit with any new design and adapt to any extra functionality with new features like windows and doors. This can be done by reviewing and updating your publishing templates at the same time. This will save time in the future.

Leaning upon the information governance standards, it should be easy to address the cleaning before moving, for all content owners who have been appointed to a set of collections or habitats. Most organisations could use a content vacuum cleaner, or rather use the search facilities and metric means to deliver up to date reports on:

Active / in-Active habitats

No clear ownership or the owner has left the building

Metadata and link quality to content and collections to be moved across to the cloud apartments.

Review publishing templates and update features or design to be used in the Cloud

When all active habitats and qualified content buckets have been revisited by their set of curators and information owners. The preparation and use of moving boxes, should be applied.

All moving boxes do need proper tagging, so that any moving company will be able to sort out where about the stuff should be placed in the new house, or building. For collections, and habitats, this means using the very same set of questions stated for adding a new habitat or collection to the cloud apartment house. Who, why, where and so forth, through the use of a structured workflow and form. When this first cleaning steps have been addressed, there should be automatic metadata enhancement, aligned with the information management processes to be used in the new cloud.

With decent resource descriptions and cleaned up content through the audit (ROT), this last step will auto-tag content based upon the business rules applied for the collection or habitat. Then been loaded into the content moving truck, or loading dock. Ready to added to the cloud.

All content that neither have proper assigned information ownership, or are in such a shape that migration can’t be done should persist on the estate or be archived or purged. This means that all metadata and links to either content bucket or habitat that won’t be moved in the first instances, should at least have correct and unique uri:s, address, to this content. And in the case a bucket or habitat have been run down by a demolition firm, purged. All inter-linkage to that piece of content or collection have to be changed.

This is typically a perfect quality report, to the information owners and content editors, that they need to work through prior to actually loading the content on the content dock.

Finally when all rotten data, deserted habitats and unmanageable buckets have been weeded out. It is time to prepare the moving truck, sending the content into its new destination.

Our final thread will cover how will the organisation and it habitants will be able to find content in this mix of clouds, and things left behind on the old estate? Cloud Search and Enterprise Search, seamless or a nightmare?

This is the fifth post in a series (1, 2, 3, 4, 6, 7 ) on the challenges organisations face as they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide on the steps you need to take.

Starting from our first post we have covered different aspects you need to consider as you take each step including information structure and how it is managed using Office 365 and SharePoint as a technology example. We will cover governance and how content should be managed in the cloud in this post.

Content created within a context, as either a departmental site, or team habitat has usually only reach and bearing for the local context of fellow members of staff within this unit. Other pieces of content have a coverage that stretches all parts of the business. One simple example, is the bucket of content that makes up the management system, with governing principles, strategies, policies and guidelines that describes the core processes, activities, roles and so forth within an organisation.

Yet other content, as the outcome from a project, will build a bucket of content that either lives in a new context, improves a bucket of content or feeds into yet another following project.

From an information management perspective, it is vital that you have organising principles to all your content, where all these layers have been covered. Both reach, and the life cycle to the set of content.

You need a governance framework that reaches out to every bucket of content. This covers what is still on your estate as well as the growing amount in the cloud. All content needs to be managed to remove risks of leakage of sensitive information and prevent people having an inconsistent user experience as they move from one bucket of content in the cloud to another content bucket still on the estate.

You need to make sure people do not see the difference between buckets of content on the estate from content buckets in the cloud. People using your content to help with their work don’t need to know where the content is kept. They need to find it as easily as before, preferably even easier! Content in the cloud should feel the same and be a natural extension to the digital environment people are already used to. Manage it with a governance framework that covers every bucket of content and make it more easy to adopt quicker and use more often without caution or delay.

Part of your governance needs to cover publishing standards based on business needs so it is easy to access from any device e.g laptops, tablets and smartphones, and to view without unnecessary authentication levels. This helps to create that consistent good user experience that encourages people to use your content whether the bucket is in the cloud or not.

A professional team from group HR, might work in their local teamsite, with on-going conversations, work-in-progress documents and so forth. Pieces of their content production leads to governing policies that have a global reach within the organisation, and needs to be linked from the corporate intranet spaces. with versioning and good quality to resource descriptions (meta data). This practice and professional network of HR people, do also share content on a departmental site. With links and resources, that have direct impact on their internal processes. The group of people, have outreaching triggers, and in-bound conversations. And have to balance these two states.

When it comes to temporal content buckets, like a project team site. There are several considerations one have to capture. First where will the outcome and result be stored, when the project is finished. In which context will these content pieces contribute. Second, what should be captured from all on-going conversations (social elements) and work-in-progress and drafts developed during the projects lifecycle? Should a project habitat, be searchable after closing down? Or do the habitat change status, hence all documentation stay within the collection, but the overarching state to the habitat changes? Within Sharepoint these temporal states, versions, workflow and properties. All sum up the organising principles.

If these principles haven’t been ironed out, and been described and decided. Inevitable there will be emerging ghost towns, of dead habitats and lost collections of content. With no governance or ownership whatsoever. All this will become a digital landfill.

This is the first post in a series(2, 3, 4, 5, 6, 7) on the challenges organisations face when they move from having online content and tools hosted firmly on their estate to renting space in the cloud. We will help you to consider the options and guide you on the steps you need to take.

In this first post we show you the most common challenges that you are likely to face and how you may overcome these.

A fast migration path, to become tenants in a cloud apartment housing unfolds a set of business critical issues that have to be mitigated:

The way forward is to settle a sustainable information architecture, that supports an information environment in constant flux. With information and data interoperable on any platform, everywhere, anytime and on any device.

You need to show how everything is managed and everyone fits together. A governance framework can help do this. It can show who is responsible for the intranet, what their responsibilities are and fit with the strategy and plan. Making it available to everyone on the intranet helps their understanding of how it is managed and supports the business.

The main point is to have a governance framework and information architecture with the same scope to avoid gaps in content being managed or not being found.

Both need to be in harmony and included in any digital strategy. This avoids competing information architectures and governance frameworks being created by different people that causes people to have inconsistent experiences not finding that they need and using alternative, less efficient, ways in future to find what they need to help with their work.

Background

Building huts, houses and villages is an emerging social construction. As humans we coordinate our common resources, tools and practices. A habitat populated by people needs housekeeping rules with available resources for cooking, cleaning, social life and so on. Routines that defines who does what task and by when in order to keep everything ok.

A framework with governing principles that set out roles and responsibilities along with standards that set out the expected level of quality and quantity of each task that everyone is engaged and complies with, is similar to how the best intranets and digital workplaces are managed.

In the early stages with a small number of habitats the rules for coordination are pretty simple, both for shared resources between the groups and pathways to connect them. The bigger a village gets, it taxes the new structures to keep things smooth. When we move ahead into mega cities with 20+ million people living close, it boils down to a general overarching plan and common infrastructures, but you also need local networked communities, in order to find feasible solutions for living together.

Like villages and mega cities there is a need for consistency that helps everyone to work and live together. Whenever you go out you know that there are pavements to walk on, roads for driving, traffic lights that we stop at when they turn red and signs to help us show the easiest way to get to our destination.

Sustainable architecture and governance creates a consistent user experience. A well structured information architecture that is aligned with a clear governance framework sets out roles and responsibilities. Publishing standards based on business needs that supports the publishers follow them. This means wherever content is published, whether it is accredited or collaborative, it will appear to be consistent to people and located where they expect it to be. This encourages a normal way to move through a digital environment with recognizable headings and consistently placed search and other features.

This allegori, fits like a glove when moving into large enterprise-wide shared spaces for collaboration. Whether it is cloud based, on-premises or a mix thereof. The social constructions and constraints still remain the same. As an IT-services on tap, cloud, has certainly constraints for a flexible and adjustable habitual construction to be able to host as many similar habitats as possible. But offers a key solutions to instantly move into! Tenants share the same apartment building (Sharepoint online).

When the set of habitats grow, navigation in this maze becomes a hazard for most of us. Wayfinding in a digital mega city, is extremely difficult. To a large extent, enterprises moving into collaboration suites suffer from the same stigma. Regardless if it is SharePoint, IBM Connections, Google Apps for Work, or a similar setting. It is not a discussion of which type of house to choose, but rather which architecture and plan that work in the emerging environment.

All collections and shared spaces, should have persistent URI:s, which is the fourth star in the ladder. When it comes to the third star of non-proprietary formats it obviously becomes a bit tricky, since i.e. MS Sharepoint and MS Office like to encourage their own format to things. But if one add resource descriptions to collections and artifacts using Dublin Core elements, it will be possible to connect different types of matter. With feasible and standardised resource descriptions it will be possible to add schemas and structures, that can tell us a little bit more about the artifacts or collection thereof. Hence the option to adhere to the second star. The first star, will inside the corporate setting become key to connect different business units, areas with open licenses and with restrictions to internal use only and in some cases open for other external parties.

Linking data-sets, that is collections or habitats, with different artifacts is the fifth star. This is where it all starts to make sense, enabling a connected digital workplace. Building a city plan, with pathways, traffic signals and rules, highways, roads, neighborhoods and infrastructural services and more. In other words, placemaking!

Placemakingis a multi-faceted approach to the planning, design and management of public spaces. Placemaking capitalizes on a local community’s assets, inspiration, and potential, with the intention of creating public spaces that promote people’s health, happiness, and well being.

We will cover more about how this applies to Office 365 and SharePoint in our next post.

Vårdaktörsportalen (VAP) is a portal for health care providers made by Västra Götalandsregionen (VGR). This portal makes information from a number of reliable and authorised sources findable and accessible for the people who need it in their daily work. This stretches from doctors and nurses to medical secretaries primarily located in the region of Västra Götaland, Sweden. The site and most of its features and information is also accesible (in Swedish) to anyone through http://vap.vgregion.se. In November 2012 the first version of this site went live and Findwise had a big role in the creation of this search centric site. The main source of information for VAP is the regional guidelines found in a document repository within VGR but some other external sources are also included. These include trustworthy authorities like

Socialstyrelsen

Läkemedelsverket

SBU

TLV

1177 – the public health care information site for citizens

This search solution is built around the open source search engine Apache Solr and our common tools for processing and indexing. For this site we have also implemented a rather unique metadata enhancement service that automatically extracts keywords from the document to index and attaches it as metadata. The keyword extraction is based on information from the medical term database SweMeSH. More information (in Swedish) can be found on Google Code. We also include synonyms to keywords to increase recall, making it easier to find documents regardless of what synonym used.

The metadata enhancement service was included because the quality of metadata on the external sites was not very good. VGR will work with the above mentioned authorities to try and make them understand that they would benefit if they improved their data. The source 1177 stands out with very good meta data and overall good quality texts.

We have conducted a user study to see how this first version is able to satisfy user demands. The result of that study shows that some local sources are missing, but a general positive feedback on the idea and the graphical design was collected.

Findwise is looking forward to continue working on VAP with VGR in the future to make it an even better tool.

Let the spelling loose …

What do Callie and Kelly have in common (except for the double ‘l’ in the middle)? What about “no” and “know”, or “Ceasar’s” and “scissors” and what about “message” and “massage”? You definitely got it – Callie and Kelly, “no” and “know”, “Ceasar’s” and “scissors” sound alike, but are spelled quite differently. “message” and “massage” on the other hand differ by only one vowel (“a” vs “e”) but their pronunciation is not at all the same.

It’s a well known fact for many languages that ortography does not determine the pronunciation of words. English is a classic example. George Bernard Shaw was the attributed author of “ghoti” as an alternative spelling of “fish”. And while phonology often reflects the current state of the development of the language, orthography may often lag centuries behind. And while English is notorious for that phenomenon it is not the only one. Swedish, French, Portuguese, among others, all have their ortography/pronunciation discrepancies.

Phonetic Algorithms

So how do we represent things that sound similar but are spelled different? It’s not trivial but for most cases it is not impossible either. Soundex is probably the first algorithm to tackle this problem. It is an example of the so called phonetic algorithms which attempt to solve the problem of giving the same encoding to strings which are pronounced in a similar fashion. Soundex was designed for English only but has its limits. DoubleMetaphone (DM) is one of the possible replacements and relatively successful. Designed by Lawrence Philips in the beginning of 1990s it not only deals with native English names but also takes proper care of foreign names so omnipresent in the language. And what is more – it can output two possible encodings for a given name, hence the “Double” in the naming of the algorithm, – an anglicised and a native (be that Slavic, Germanic, Greek, Spanish, etc.) version.

By relying on DM one can encode all the four names in the title of this post as “PRN”. The name George will get two encodings – JRJ and KRK, the second version reflecting a possible German pronunciation of the name. And a name with Polish origin, like Adamowicz, would also get two encodings – ATMTS and ATMFX, depending on whether you pronounce the “cz” as the English “ch” in “church” or “ts” in “hats”.

The original implementation by Lawrence Philips allowed a string to be encoded only with 4 characters. However, in most subsequent
implementations of the algorithm this option is parameterized or just omitted.

Apache Commons Codec has an implementation of the DM among others (Soundex, Metaphone, RefinedSoundex, ColognePhonetic, Coverphone, to
name just a few.) and here is a tiny example with it:

import org.apache.commons.codec.language.DoubleMetaphone;

public class DM {

public static void main(String[] args) {

String s = "Adamowicz";

DoubleMetaphone dm = new DoubleMetaphone();

// Default encoding length is 4!

// Let's make it 10

dm.setMaxCodeLen(10);

System.out.println("Alternative 1: " + dm.doubleMetaphone(s) +

// Remember, DM can output 2 possible encodings:

"nAlternative 2: " + dm.doubleMetaphone(s, true));

}}

The above code will print out:

Alternative 1: ATMTS

Alternative 2: ATMFX

It is also relatively straightforward to do phonetic search with Solr. You just need to ensure that you add the phonetic analysis to a field which contains names in your schema.xml:

Enhancements

While DM does perform quite well, at first sight, it has its limitations. We should know that it still originated from the English language and although it aims to tackle a variety of non-native borrowings most of the rules are English-centric. Suppose you work on any of the Scandinavian languages (Swedish, Danish, Norwegian, Icelandic) and one of the names you want to encode is “Örjan”. However, “Orjan” and “Örjan” get different encodings – ARJN vs RJN. Why is that? One look under the hood (the implementation in DoubleMetaphone.java) will give you the answer:

private static final String VOWELS = "AEIOUY";

So the Scandinavian vowels “ö”, “ä”, “å”, “ø” and “æ” are not present. If we just add these then compile and use the new version of the DM implementation we get the desired output – ARJN for both “Örjan” and “Orjan”.

Finally, if you don’t want to use DM or maybe it is really not suitable for your task, you still may use the same principles and create your own encoder by relying on regular expressions for example. Suppose you have a list of bogus product names which are just (mis)spelling variations of some well known names and you want to search for the original name but get back all ludicrous variants. Here is one albeit very naïve way to do it. Given the following names:

CupHoulder

CappHolder

KeepHolder

MacKleena

MackCliiner

MacqQleanAR

Ma’cKcle’an’ar

and with a bunch of regular expressions you can easily encode them as “cphldR” and “mclnR”.

You can now easily find all the ludicrous spellings of “CupHolder” och “MacCleaner”.

I hope this blogpost gave you some ideas of how you can use phonetic algorithms and their principles in order to better discover names and entities that sound alike but are spelled unlike. At Findwise we have done a number of enhancements to DM in order to make it work better with Swedish, Danish and Norwegian.

According to Wikipedia, metadata is defined as data about data. That might sound a bit abstract, but what it means is that metadata provides a bit more information about some content whether it’s a piece of text, an image, a video or something else. For a text metadata can be the file format it’s stored as (plain text, word, pdf, etc) and for an image metadata can be the resolution of the image.

Metadata can be divided into different types. Exactly what the types are is not set but I like to think of it as either a) technical or b) descriptive.

Technical metadata is often a finite set that can be common accross organisations, where descriptive metadata is more related to the organisation’s needs and structure.

So all this talk about metadata, why do we need to worry about this in a findability solution? Well, since metadata tells us a bit more about our content, we should use this to help our users to find their information quicker. I like to think that it can be used in at least three ways in a findability solution; relevance influence, navigation, and result presentation.

So if you define descriptive metadata that makes sense to the users in your organisation, they are very likely to assign them to content they are creating. When content has a high degree of metadata assigned you can use this to help users navigate to the content by using the metadata instead of a fixed folder-like structure. When searching, you can tune the relevance so that if the user’s query matches content in the metadata of the document, it is ranked higher than other documents.

The important thing about metadata is that if you can make users assign it to their content it can be used in many different ways and applications to help people find their content quickly.

If you have 6 minutes to spare I would recommend you to watch this interview with Gabriel Olsson from Tetra Pak. During the last years Tetra Pak has been working strategically with turning their intranet into something true end user-centric. Tetra Pak has also put effort into search and content quality.

By actually asking the employees what they expect to find and what sort of information that would make their everyday work (tasks) more efficient, Tetra Pak has managed to create a navigation structure based on facts reflecting these needs. The method used is Gerry McGovern’s Task based Customer Carewords… and the result? The ones that scream the loudest are not the most important – the need of the employees is.

Gabriel is also talking about the importance of following up on search by key matches and synonyms. This, together with content quality initiatives, helps create a solid foundation for search, the simple reasons being:

Use metadata to filter search results (note, not a Tetra Pak picture)

If the quality of the information is good (clear headings, good metadata, frequent keywords), the information found through search will be good as well. If you have a lot of old content and duplicates this will be just as visible, making it hard for the users to determinate what is qualitative and trustworthy.Good quality will also make it possible to group and categorize information.

Synonyms makes it easy to adjust the corporate language to the one used by the employees. Let people search for “report” when they want to find a “bulletin”. A simple synonym list, based on search statistics will make users find what they want, without thinking about how to phrase the query.The synonyms can used in the background (without the users knowledge) or as ‘did you mean-suggestions’:

Synonyms used for ‘Did you mean” functionality (note, not a Tetra Pak picture)

Key matches (also referred to as sponsored links, best bets or editor’s pick) are used to manually force the first hit in the search result list to refer to a specific page or document. By following up on search statistics and knowing what sort of information that is frequently most asked for, it is easy to adjust the search result list. However, this take time and effort to follow up.

Tetra Pak is not alone when it comes to adjusting their intranets to true end-user needs. During the spring there will be a number of conferences where our customers will be sharing experiences from their initiatives. Among others Ability Partner, and the recently completed IntraTeam.

Apart from this, our own breakfast seminaries is a, as always, announced on our homepage and on twitter. Looking forward to seeing you!

I realise we are a bit late. Fredrik Wackå, a senior IT-strategist, has already written an excellent article on his blog (in Swedish). He has, among other things, been interviewing Kristian Norling (at Twitter), who has been working with portal strategies and better search for many years at Region Västra Götaland. Although, for all our non-Swedish speaking guests here is a short summary:

Findwise has during the last few months been working on a new search solution for Region Västra Götaland. The two main goals have been to deliver a search experience that seems both fast and accurate. The result? Today making a search at VGR takes about 0,1-0,2 seconds, faster than a Google search on the web.

Furthermore, there was a need for context. Large amount of information requires ways to filter and sort – otherwise the users will drown in the result list. By giving the end-users the ability to sort the search result the users can look for general information within an area as well as quickly narrow down to a specific piece (for example by two clicks be able to see only the PDF-files created in 2009). The filters (and thereby metadata standard) includes:

Information type

Where the document resides

Where it belongs in the organization

What source it has

When it was last changed

Who has written it

What format it resides in

Keywords that has been created

The search solution also includes a metadata service. As so many others VGR has been struggling with getting the metadata in place.

Apart from the metadata supported by the system (where Dublin Core is being used) the metadata service is doing two things:

Analyses the content in the text, compares it to taxonomy and gives the writer suggestions of keywords that he/she can use

Gives the writer the ability to add additional keywords

Apart from this the end-users will be able to add etiquettes (tags). These will be compared with two lists. If the tags appears in the “white list” it will be published right away, if they are in the “blacklist” they will be deleted. Anything inbetween are controlled before they are published.

To conclude: a lot of effort has been put into creating a good search experience and VGR continues to deliver functionality and solutions that are light-years ahead of many others. The combination of supporting systems and using the “collected intelligence” of the writers and end-users will make it even better over time. Search is about both supporting systems, content and people.

Having an Enterprise Search Engine, there are basically two ways of getting content into the index; using a web crawler or a connector. Both methods have their advantages and disadvantages. In this post I’ll try to poinpoint the differences with the two methods.

Web crawler

Most systems of today have a web-interface. Let it be your time reporting system, intranet, document management, you’ll probably access those with your web browser. Because of this, it’s very easy to use a web crawler to index this content as well.

The web crawler index the pages by starting at one page. From there, it follows all outbound links and index those. From those pages, it follows all links, and so on. This process continues until all links at a web site has been followed and the pages been indexed. The crawler thus uses the same technique as a human, visit a page and clicking the links.

Most Enterprise Search Engines are bundled with a web crawler. Thus, it’s usually very easy to get started. Just enter a start page and within minutes you’ll have searchable content in your index. No extra installation or license fee are required. For some sources, this may also be the only option, i.e if you’re indexing external sources that your company has no control of.

The main disadvantage though, is that web pages are designed for humans, not crawlers. This means that there are a lot of extra information for presentation purposes, such as navigation menus, sticky information messages, headers and footers and so on. All of this makes it a more pleasant experience for the user, and also making it easier to navigate on the page. The crawler on the other hand has no use of this information when retrieving pages. It’s actually reducing information quality in the index. For example, a navigation menu will be displayed on every page, thus the crawler will index the navigation content for all pages. So if you have a navigation item called “Customers” and a user searches for customers, he/she will get a hit in ALL pages in the index.

There are ways to get around this, but it requires either altering of the produced HTML or adjustments in the search engine. Also, if the design of the site change, you have to do these adjustments again.

Connector

Even though the majority of systems has a web-interface, the content is stored in a data source of some format. It might be a database, structured file system, etc. By using a connector, you connect either to the underlying data source or to the system directly by its programming API.

Using a connector, the search engine does not get any presentation information but only the pure content, making the information quality in the index better. The connector can also retrieve all metadata associated with the information which further increases the quality. Often, you’ll also have more fine-grained control over what will be indexed with a connector than a web crawler.

Though, using a connector requires more configuration. It might also cost some extra money to buy one for your system, and require additional hardware. Though, once set up, it’s most likely to produce more relevant results compared to a web crawler.

Bottom line is it’s a consideration between quality and cost, as most decisions in life 🙂